Abstract:
Soil salinization is relatively severe in the Yellow River Delta region. However, the conventional salinization monitoring can often suffer from the time-consuming, labor-intensive, and costly problems. Among them, the single-band information can be extracted from the hyperspectral data, in order to conduct the salinization monitoring. The inter-band correlation information cannot be fully utilized during extraction. Moreover, the original data can also share the low sensitivity to the salt response. This study aims to fully exploit the correlation information between spectra, particularly for the high sensitivity of spectral data. The accuracy of the salinity inversion was also improved to accurately determine the degree of salinization. Hyperspectral data source was collected from the field sampling in the Yellow River Delta region, Dongying City, Shandong Province, China. Pretreatment was also applied, including Savitzky-Golay (S-G) filtering and Multiplicative Scatter Correction (MSC). The sample data was split into the training and testing datasets at a 7:3 ratio. Fractional-order differentiation (FOD) processing was performed on the spectral data. Furthermore, nine typical indices of the two-dimensional spectra were constructed at each differential order, according to the possible pairwise combinations of the bands. The optimal band and differential combinations were obtained to evaluate the correlation coefficient between these indices and soil salt content. These feature variables were then selected to construct the various models, including Convolutional Neural Networks (CNN), Partial Least Squares Regression (PLSR), and Random Forest (RF). Grid search algorithm and ten-fold cross-validation were used for the hyperparameter tuning. Finally, the accuracy of the improved model was assessed using root mean square error (RMSE), relative percent difference (RPD), and the coefficient of determination (
R2). Results showed that the noise and baseline drift were reduced in the preprocessed spectral curves. The smoother and more concentrated curves of the spectral features shared no significant change in the trend, compared with the original. The dataset all exhibited the strong variability, indicating the reasonable sample partitioning. FOD was effectively highlighted the gradual change during spectral curve variations, in order to enhance the sensitivity of the spectral data. The soil salinity was better predicted in the coastal saline areas. There was the significant correlation between different spectral indices and soil salt content (
P<0.01). Compared with the original spectra, there were the significantly higher correlation coefficients between FOD-constructed spectral indices and soil salt content (
P<0.01). The FOD with the spectral index was effectively identified the sensitive spectral information for the data dimensionality reduction. The Normalized Difference Index (NDI) at the 1.6 order (1244, and 2081 nm) also exhibited the highest correlation coefficient (0.9) with the soil salt content, followed by the Ratio Index (RI) at the 1.6 order (2242, 1208 nm) with a correlation coefficient of 0.88. The CNN model was achieved in the highest inversion accuracy, with the testing set RPD,
R2, and RMSE values of 3.41, 0.91, and 1.42 g/kg, respectively. Compared with the PLSR and RF models, the CNN model's testing set RPD increased by 1.74 and 1.76, respectively,
R2 increased by 0.03 and 0.28, respectively, whereas, RMSE decreased by 1.47 g/kg and 1.52 g/kg, respectively. The better inversion performance of the CNN model was achieved in the slight, moderate, severe, and very severe salinization. The PLSR model performed better for the very severe salinization, while the RF model performed poorly overall. The FOD was combined with the spectral indices, indicating the spectral sensitivity and inversion accuracy. Furthermore, the better adaptability of the CNN model was obtained after optimization, compared with the PLSR and RF models. A more robust FOD-CNN model was better suited for the salinity inversion, providing for the potential support to the monitoring salinization in the Yellow River Delta.